Data is everywhere. There is no shortage of data – even the neophyte entrepreneurs who have just begun their business operations are sitting on mounds and mounds of data – but this often makes us introspect how can we use data to grow bigger, more productive?
Today, data lakes are springing up here and there. And with that, the composition structure of data lakes is changing. As more and more data are moving towards cloud, data lakes are shifting focus towards cutting edge sources, like NoSQL, while cloud data warehouses are emerging across hybrid deployments.
A humongous amount of data is being churned out on digital platform each day. IBM says as much as 2.5 quintillion bytes of data is created on a daily basis. Now, this ever-expanding amount of data needs for proper storage system – for that, data lakes have been constructed to hold data in its raw form. In these vast storehouses, data remain mostly in their unstructured state, which is pulled out by data scientists to remodel and transform them into versatile data sets for future use.
Data is the buzzword. It is conquering the world, but who conquers data: the companies that use them or the servers in which they are stored?
Let’s usher you into the fascinating world of data, and data governance. FYI: the latter is weaving magic around the Business Intelligence community, but to optimize the results to the fullest, it needs to depend heavily on a single factor, i.e. efficient data management. For that, highly-skilled data analysts are called for – to excel on business analytics, opt for Business Analytics Online Certification by DexLab Analytics. It will feed you in the latest trends and meaningful insights surrounding the daunting domain of data analytics.
A few years ago, Silicon Valley in San Francisco came under the influence of a new, mysterious thing known as Bitcoin. It swept away the tech enthusiasts off their feet. There were a wide set of rumors that Bitcoin was virtual money, invented by a pseudonymous math stalwart named Satoshi Nakamoto, who would later stir up the structure of modern finance and render government-powered currency antiquated.
To understand the phenomenon better, I once bought a single Bitcoin long time back, which then involved a strenuous labor-intensive process, where I had to go to CVS and use MoneyGram to wire dollar value of a Bitcoin to a crypto-currency exchange. After a month or so, I decided to sell it off for a slight loss, thoroughly convinced that this virtual money is nothing but just a passing fad.
Ever wondered why many organizations often find it hard to implement Big Data? The reason often is poor or non-existent data management strategies which works counterproductive.
Data cannot be delivered or analysed without proper technology systems and procedural flows data can never be analysed or delivered. And without an expert team to manage and maintain the setup, errors, and backlogs will be frequent.
Before we make a plan of the data management strategies we must consider what systems and technologies one may need to add and what improvements can be made to an existing processes; and what do these roles bring about in terms of effects with changes.
However, a much as is possible any type of changes should be done by making sure a strategy is going to be integrated with the existing business process.
And it is also important to take a holistic point of view, for data management. After all, a strategy that does not work for its users will never function effectively for any organization.
With all these things in mind, in this article we will examine each of the three most important non-data components for a successful data management strategy – this should include the process, the technology and the people.
Recognizing the right data systems:
There is a lot of technology implemented into the Big Data industry, and a lot of it is in the form of a highly specific tool system. Almost all of the enterprises do need the following types of tech:
This will isolate specific information from a large data sets and transform it into usable metrics. Some o the familiar data mining tools are SAS, R and KXEN.
The process of ETL is used to extract, transform, and also will load data so that it can be used. ETL tools also automate this process so that human users will not have to request data manually. Moreover, the automated process is way more consistent.
Enterprise data warehouse:
A centralised data warehouse will be able to store all of an organization’s data and also integrate a related data from other sources, this is an indispensible part of any data management plan. It also keeps data accessible, and associates a lot of kinds of customer data for a complete view.
These are tools, which provide a layer of security and quality assurance by monitoring some critical environments, with problem diagnosing, whenever they arise, and also to quickly notify the team behind analytics.
Business intelligence and reporting, Analytics:
These are tools that turn processed data into insights, that are tailored to extract roles along with users. Data must go to the right people and in the right format for it to be useful.
And in analytics highly specific metrics are combined like customer acquisition data, product life cycle, and tracking details, with intuitive user friendly interfaces. They often integrate with some non-analytics tools to ensure the best possible user experience.
So, it is important to not think of the above technologies as simply isolated elements but instead consider them as a part of a team. Which must work together as an organized unit.
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